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arXiv · 2109.02383

Data Science Kitchen at GermEval 2021: A Fine Selection of Hand-Picked Features, Delivered Fresh from the Oven

Niclas Hildebrandt·Benedikt Boenninghoff·Dennis Orth·Christopher Schymura
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Abstract

This paper presents the contribution of the Data Science Kitchen at GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments. The task aims at extending the identification of offensive language, by including additional subtasks that identify comments which should be prioritized for fact-checking by moderators and community managers. Our contribution focuses on a feature-engineering approach with a conventional classification backend. We combine semantic and writing style embeddings derived from pre-trained deep neural networks with additional numerical features, specifically designed for this task. Classifier ensembles are used to derive predictions for each subtask via a majority voting scheme. Our best submission achieved macro-averaged F1-scores of 66.8\%,\,69.9\% and 72.5\% for the identification of toxic, engaging, and fact-claiming comments.

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Niclas Hildebrandt, Benedikt Boenninghoff, Dennis Orth, Christopher Schymura. 2024-08-18. Data Science Kitchen at GermEval 2021: A Fine Selection of Hand-Picked Features, Delivered Fresh from the Oven. https://arxiv.org/abs/2109.02383

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